Predicting Students' Performance with SimStudent: Learning Cognitive Skills from Observation

  • Authors:

  • Noboru Matsuda;William W. Cohen;Jonathan Sewall;Gustavo Lacerda;Kenneth R. Koedinger

  • Affiliations:

  • Human-Computer Interaction Institute;Machine Learning Department, Carnegie Mellon University;Human-Computer Interaction Institute;Machine Learning Department, Carnegie Mellon University;Human-Computer Interaction Institute

  • Venue:
  • Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
  • Year:
  • 2007

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Abstract

SimStudent is a machine-learning agent that learns cognitive skills by demonstration. SimStudent was originally built as a building block for Cognitive Tutor Authoring Tools to help an author build a cognitive model without significant programming. In this paper, we evaluate a second use of SimStudent, viz., student modeling for Intelligent Tutoring Systems. The basic idea is to have SimStudent observe human students solving problems. It then creates a cognitive model that can replicate the students' performance. If the model is accurate, it would predict the human students' performance on novel problems. An evaluation study showed that when trained on 15 problems, SimStudent accurately predicted the human students' correct behavior on the novel problems more than 80% of the time. However, the current implementation of SimStudent does not accurately predict when the human students make errors.